Abstract: A method for improving a process model for a patterning process, the method including obtaining a) a measured contour from an image capture device, and b) a simulated contour generated from a simulation of the process model. The method also includes aligning the measured contour with the simulated contour by determining an offset between the measured contour and the simulated contour. The process model is calibrated to reduce a difference, computed based on the determined offset, between the simulated contour and the measured contour.

Abstract: Monoclonal antibodies against human Mac-1 are provided. These antibodies can bind to different states of Mac-1 so as to alter the biofunctions of Mac-1. These antibodies can modulate Th1/Th2 cytokine secretions by TLR-activated immune cells and can be used for the treatments of diseases related to acute and chronic inflammatory disorders, such as infectious diseases, and cancers.

Abstract: A method including: obtaining data based an optical proximity correction for a spatially shifted version of a training design pattern; and training a machine learning model configured to predict optical proximity corrections for design patterns using data regarding the training design pattern and the data based on the optical proximity correction for the spatially shifted version of the training design pattern.

Abstract: A method including: obtaining a thin-mask transmission function of a patterning device and a M3D model for a lithographic process, wherein the thin-mask transmission function is a continuous transmission mask (CTM) and the M3D model at least represents a portion of M3D attributable to multiple edges of structures on the patterning device; determining a M3D mask transmission function of the patterning device by using the thin-mask transmission function and the M3D model; and determining an aerial image produced by the patterning device and the lithographic process, by using the M3D mask transmission function.

Abstract: A method including: obtaining data based an optical proximity correction for a spatially shifted version of a training design pattern; and training a machine learning model configured to predict optical proximity corrections for design patterns using data regarding the training design pattern and the data based on the optical proximity correction for the spatially shifted version of the training design pattern.

Abstract: A method to determine a curvilinear pattern of a patterning device that includes obtaining (i) an initial image of the patterning device corresponding to a target pattern to be printed on a substrate subjected to a patterning process, and (ii) a process model configured to predict a pattern on the substrate from the initial image, generating, by a hardware computer system, an enhanced image from the initial image, generating, by the hardware computer system, a level set image using the enhanced image, and iteratively determining, by the hardware computer system, a curvilinear pattern for the patterning device based on the level set image, the process model, and a cost function, where the cost function (e.g., EPE) determines a difference between a predicted pattern and the target pattern, where the difference is iteratively reduced.

Abstract: A method for determining a likelihood that an assist feature of a mask pattern will print on a substrate. The method includes obtaining (i) a plurality of images of a pattern printed on a substrate and (ii) variance data the plurality of images of the pattern; determining, based on the variance data, a model configured to generate variance data associated with the mask pattern; and determining, based on model-generated variance data for a given mask pattern and a resist image or etch image associated with the given mask pattern, the likelihood that an assist feature of the given mask pattern will be printed on the substrate. The likelihood can be applied to adjust one or more parameters related to a patterning process or a patterning apparatus to reduce the likelihood that the assist feature will print on the substrate.

Abstract: A method for improving a design of a patterning device. The method includes (i) obtaining mask points of a design of a mask feature, wherein the mask feature corresponds to a target feature in a target pattern to be printed on a substrate; and (ii) adjusting locations of the mask points to generate a modified design of the mask feature based on the adjusted mask points.

Abstract: A method including: obtaining data based an optical proximity correction for a spatially shifted version of a training design pattern; and training a machine learning model configured to predict optical proximity corrections for design patterns using data regarding the training design pattern and the data based on the optical proximity correction for the spatially shifted version of the training design pattern.

Abstract: A method of determining a mask pattern for a target pattern to be printed on a substrate. The method includes partitioning a portion of a design layout including the target pattern into a plurality of cells with reference to a given location on the target pattern; assigning a plurality of variables within a particular cell of the plurality of cells, the particular cell including the target pattern or a portion thereof; and determining, based on values of the plurality of variables, the mask pattern for the target pattern such that a performance metric of a patterning process utilizing the mask pattern is within a desired performance range.

Abstract: A method including: obtaining data based an optical proximity correction for a spatially shifted version of a training design pattern; and training a machine learning model configured to predict optical proximity corrections for design patterns using data regarding the training design pattern and the data based on the optical proximity correction for the spatially shifted version of the training design pattern.

Abstract: A method for training a machine learning model to generate a predicted measured image, the method including obtaining (a) an input target image associated with a reference design pattern, and (b) a reference measured image associated with a specified design pattern printed on a substrate, wherein the input target image and the reference measured image are non-aligned images; and training, by a hardware computer system and using the input target image, the machine learning model to generate a predicted measured image.

Abstract: A method including: obtaining a thin-mask transmission function of a patterning device and a M3D model for a lithographic process, wherein the thin-mask transmission function is a continuous transmission mask (CTM) and the M3D model at least represents a portion of M3D attributable to multiple edges of structures on the patterning device; determining a M3D mask transmission function of the patterning device by using the thin-mask transmission function and the M3D model; and determining an aerial image produced by the patterning device and the lithographic process, by using the M3D mask transmission function.

Abstract: Methods of identifying a hot spot from a design layout or of predicting whether a pattern in a design layout is defective, using a machine learning model. An example method disclosed herein includes obtaining sets of one or more characteristics of performance of hot spots, respectively, under a plurality of process conditions, respectively, in a device manufacturing process; determining, for each of the process conditions, for each of the hot spots, based on the one or more characteristics under that process condition, whether that hot spot is defective; obtaining a characteristic of each of the process conditions; obtaining a characteristic of each of the hot spots; and training a machine learning model using a training set including the characteristic of one of the process conditions, the characteristic of one of the hot spots, and whether that hot spot is defective under that process condition.

Abstract: A method of controlling a computer process for designing or verifying a photolithographic component, the method including building a source tree including nodes of the process, including dependency relationships among the nodes, defining, for some nodes, at least two different process conditions, expanding the source tree to form an expanded tree, including generating a separate node for each different defined process condition, and duplicating dependent nodes having an input relationship to each generated separate node, determining respective computing hardware requirements for processing the node, selecting computer hardware constraints based on capabilities of the host computing system, determining, based on the requirements and constraints and on dependency relations in the expanded tree, an execution sequence for the computer process, and performing the computer process on the computing system.

Abstract: Methods of identifying a hot spot from a design layout or of predicting whether a pattern in a design layout is defective, using a machine learning model. An example method disclosed herein includes obtaining sets of one or more characteristics of performance of hot spots, respectively, under a plurality of process conditions, respectively, in a device manufacturing process; determining, for each of the process conditions, for each of the hot spots, based on the one or more characteristics under that process condition, whether that hot spot is defective; obtaining a characteristic of each of the process conditions; obtaining a characteristic of each of the hot spots; and training a machine learning model using a training set including the characteristic of one of the process conditions, the characteristic of one of the hot spots, and whether that hot spot is defective under that process condition.

Abstract: A method including: obtaining a thin-mask transmission function of a patterning device and a M3D model for a lithographic process, wherein the thin-mask transmission function is a continuous transmission mask (CTM) and the M3D model at least represents a portion of M3D attributable to multiple edges of structures on the patterning device; determining a M3D mask transmission function of the patterning device by using the thin-mask transmission function and the M3D model; and determining an aerial image produced by the patterning device and the lithographic process, by using the M3D mask transmission function.

Abstract: A method for determining an overlapping process window (OPW) of an area of interest on a portion of a design layout for a device manufacturing process for imaging the portion onto a substrate, the method including: obtaining a plurality of features in the area of interest; obtaining a plurality of values of one or more processing parameters of the device manufacturing process; determining existence of defects, probability of the existence of defects, or both in imaging the plurality of features by the device manufacturing process under each of the plurality of values; and determining the OPW of the area of interest from the existence of defects, the probability of the existence of defects, or both.

Abstract: A method for training a patterning process model, the patterning process model configured to predict a pattern that will be formed by a patterning process. The method involves obtaining an image data associated with a desired pattern, a measured pattern of the substrate, a first model including a first set of parameters, and a machine learning model including a second set of parameters; and iteratively determining values of the first set of parameters and the second set of parameters to train the patterning process model. An iteration involves executing, using the image data, the first model and the machine learning model to cooperatively predict a printed pattern of the substrate; and modifying the values of the first set of parameters and the second set of parameters such that a difference between the measured pattern and the predicted pattern is reduced.

Abstract: A method of controlling a computer process for designing or verifying a photolithographic component includes building a source tree including nodes of the process, including dependency relationships among the nodes, defining, for some nodes, at least two different process conditions, expanding the source tree to form an expanded tree, including generating a separate node for each different defined process condition, and duplicating dependent nodes having an input relationship to each generated separate node, determining respective computing hardware requirements for processing the node, selecting computer hardware constraints based on capabilities of the host computing system, determining, based on the requirements and constraints and on dependency relations in the expanded tree, an execution sequence for the computer process, and performing the computer process on the computing system.